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Peer-Reviewed Publication
J Clin Epidemiol2024;169111305.May 1, 2024Journal Article

A methodological review of the high-dimensional propensity score in comparative-effectiveness and safety-of-interventions research finds incomplete reporting relative to algorithm development and robustness.

Guillaume Louis Martin1, Camille Petri2, Julian Rozenberg3, Noémie Simon4, David Hajage4, Julien Kirchgesner5, Florence Tubach4, Louis Létinier6, Agnès Dechartres4
1Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, Paris, France; Synapse Medicine, Bordeaux, France. Electronic address: guillaume.martin.md@gmail.com.
2UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK.
3Sorbonne Université, AP-HP, Paris, France.
4Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, Paris, France.
5Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Saint-Antoine, Département de Gastroentérologie et Nutrition, Paris, France.
6Synapse Medicine, Bordeaux, France.
PubMed DOI

Abstract

OBJECTIVES: The use of secondary databases has become popular for evaluating the effectiveness and safety of interventions in real-life settings. However, the absence of important confounders in these databases is challenging. To address this issue, the high-dimensional propensity score (hdPS) algorithm was developed in 2009. This algorithm uses proxy variables for mitigating confounding by combin…

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